where \(\tau \)  is a regularization parameter.
This formulation minimizes over features \(\mathbf {z}_T, \mathbf {z}_R\)  and two auxiliary parameters \(\hat{\mathbf {T}}, \hat{\mathbf {R}}\) . Based on Proximal Gradient Descent (PGD) [1]}, [2]}, we propose an iterative algorithm to sequentially update \(\mathbf {z}_T, \mathbf {z}_R, \hat{\mathbf {T}}, \hat{\mathbf {R}}\)  with simple computations.
